Developments in artificial intelligence (AI) are expected to change the world of work across the board. We have already witnessed the impact on marketing communication endeavours and strategies. The ripples spreading across the water include ethical issues and questions of accountability. I caught up with Anabel Gutiérrez, Senior Lecturer in Digital Marketing at the University of Kent, and a member of SAS UK and Ireland Academic Advisory Board to talk AI, ethics and accountability in her post-graduate marketing curriculum.
How is the scope of marketing changing?
Anabel Gutiérrez (AG): It is helpful to start by talking about how the marketing profession is changing—and that means talking about consumer preferences. Consumers nowadays expect relevant, meaningful and timely contact points with the companies. In other words, they await tailored and customised/personalised communication about things that lie within their interests. What’s more, marketers who can achieve this have better results: one survey suggested that marketers who exceeded their revenue goals were using personalisation over 80% of the time. There is no room for excuses for marketers anymore! They can interact with their consumers through brick-and-mortar, online, mobile, and TV commerce; so they can monitor their behaviour and provide a one-to-one customer experience.
What is involved in personalisation?
AG: To be able to personalise communications, marketers need to use data to understand their customers and predict their preferences and behaviours. There is an enormous amount of data available about every customer: their contact with the company, their previous behaviour, their responses to email campaigns, what they share on social media, which websites they visit in searching for information, and so on. Marketers who can use this information effectively, drawing on analytical tools and techniques along with the traditional established methods, can target content and campaigns with precision and accuracy.
Marketers need a number of skills to be data-driven. They need to understand and appreciate the difference between gut feeling and evidence, and know when to draw on each. They need to be able to use marketing technology effectively to provide insights. They also need to be able to use insights from data to inform decisions and take effective actions as a result. Data-driven marketers are constantly looking at the data for new insights and finding ways to take advantage of what they discover.
How are you preparing your students for this?
AG: Like many leading universities, we are taking steps to ensure our graduates and postgraduates have the necessary skills to compete in the AI-driven new world of work. Masters’ courses now include the study of fuzzy logic, robotics, eye tracking technology, 3D online environments and visualisation, among other approaches in computer science courses. We also draw on industry experience, by including collaboration with current leaders in the analytics world.
What is the level of analytics competence required?
AG: The level of qualification required for these courses is pretty high. Most AI-focused Masters’ courses require either an undergraduate degree in computer science or considerable professional programming experience. Beyond computing courses, however, there are plenty of more general courses that recognise the importance of technology and data, such as in the teaching of marketing.
For example, I led the development of a course on digital marketing and analytics at Regent’s University back in 2014, and there are other similar courses around. At Kent Business School, our MSc Digital Marketing and Analytics course provides students with a comprehensive knowledge of digital ecosystems for marketing and technology trends with focus on developing innovation and data-driven mindset in every module. They also get hands-on experience through real-world marketing problems and simulated business scenarios. A core component of the programme is the focus on analytics, applications, and advanced digital communications in order to build knowledge on customer behaviour within the contemporary business environment.
What is the biggest change in recent years?
AG: One thing that has changed in recent years is the increase in open source software. A few years ago, the marketing technology ‘stack’ would largely have been proprietary, but that is now starting to change, for several reasons. There is an increasingly wide range of very useful software available, both specifically marketing-targeted and more generally. However, open source can be unreliable and untested in the quality of the algorithms and models that are available. Therefore, it is important to be able to combine open source with proprietary software, allowing the data professionals to have the freedom to choose the open source they want to use and build their own tailored marketing analytics models, while collaborating in the same environment.
Why is an open platform important to future marketers?
AG: Think back to the earlier points about being data-driven. We should anticipate that future data sources will be from a broader spectrum of sources. Being able to use them with confidence will be critical. This means that marketers can be comfortable with open standards and APIs, whilst working with proprietary software that can offer a secure and governed framework, which is also efficient and effective. This kind of approach to building a marketing stack means that marketers will be able to deliver what customers want: a personalised and tailored experience driven by insights from data that is being managed in an ethical way to safeguard trust.
Taking the discussion further
Join our #saschat on 2nd November at 4:00 - 5:00 pm CET I 3:00 – 4:00pm UK GMT I10:00am EDT to discuss more about this fascinating area.
We will be considering the following questions:
Q1: What do you see as the most exciting trends in digital marketing?
Q2: Which marketing functions will be the first to benefit from AI?
Q3: What are the building blocks for effective AI applications in marketing?
Q4: How should responsibility for AI models be managed?
Q5: What is the role of open source analytics in the marketers’ toolkit?
Q6: What are the biggest challenges with data acquisition and management?
Q7: What is the role of external or open data in the future of real-time and contextual marketing?